Multi-region electricity demand prediction with ensemble deep neural networks

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.

Flow mediated dilation with photoplethysmography as a substitute for ultrasonic imaging

Flow mediated dilation (FMD) is a non-invasive method for endothelial function assessment providing an index extracted from ultrasonic B-mode images. Although utilized in the research community, the difficulty of its application and high cost of ultrasonic devices prevent it from being widely used in clinical settings. In this study we show that substituting the ultrasonic device with more easily handled and low cost photoplethysmography and electrocardiography is possible. We introduce new indices based on the photoplethysmogram (PPG) and electrocardiogram (ECG) and show that they are correlated with the ultrasound-based FMD Index. To this end, a conventional ultrasound FMD test was carried out whereas PPG and ECG were simultaneously recorded from 20 healthy volunteers (13 M, 7 F) in the age range of 23-32 years. Our results show a significant correlation between our proposed index and ultrasound FMD when using the ECG in conjunction with the PPG (R = 0.77, p < 0.000 01). Using the PPG alone produces a lower correlation (R = 0.72, p < 0.0001). Compared to conventional FMD, the proposed method is low cost and does not require any special operator skills. Hence it may be easily utilized as a screening tool in locations deprived of high-end ultrasound imaging devices.